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How data science can advance mental health research

Research output: Contribution to journalArticlepeer-review

the MQ Data Science group, Tom C. Russ, Eva Woelbert, Katrina A. S. Davis, Jonathan D. Hafferty, Zina Ibrahim, Becky Inkster, Ann John, William Lee, Margaret Maxwell, Andrew M. McIntosh, Rob Stewart, Margaret Anderson, Kate Aylett, Suzy Bourke, Anna Burhouse, Felicity Callard, Kathy Chapman, Matt Cowley, James Cusack & 31 more Jaime Delgadillo, Sophie Dix, Richard Dobson, Gary Donohoe, Nadine Dougall, Johnny Downs, Helen Fisher, Amos Folarin, Thomas Foley, John Geddes, Joardana Globerman, Lamiece Hassan, Joseph Hayes, Helen Hodges, Eddie Jacob, Rowena Jacobs, Cynthia Joyce, Suky Kaur, Maximilian Kerz, James Kirkbride, Gerard Leavey, Glyn Lewis, Keith Lloyd, Wendy Matcham, Erin McCloskey, Andrew McQuillin, Tamsin Newlove Delgado, Catherine Newsome, Kristin Nicodemus, Daniel Smith, Robert Stewart

Original languageEnglish
Pages (from-to)24-32
JournalNature Human Behaviour
Accepted/In press11 Oct 2018
Published10 Dec 2018


King's Authors


Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.

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